def write_to_movie_file(self, output_folder, verbose=False):
"""
Write an MPG movie to output folder
:param output_folder: output directory
:param verbose: verbose output, default = False
"""
individuals = []
for treatment in self.Treatments:
for curr_subject in treatment["individuals"]:
individuals.append(curr_subject)
#print("individuals:",individuals)
for individual in individuals:
individual.MovementProcesses["x"].History.pop(0)
individual.MovementProcesses["y"].History.pop(0)
individual.MovementProcesses["z"].History.pop(0)
for item in individuals:
print(vars(item))
visualization.save_simulation_movie(individuals,
output_folder,
len(individuals),
self.NTimepoints,
black_background=True,
data_o=True,
verbose=self.verbose)
def run(self):
"Run the experiment, simulating timesteps"
visualization.save_simulation_figure(individuals, opts.output,
n_individuals, n_timepoints,
perturbation_timepoint)
visualization.save_simulation_movie(individuals, opts.output,
n_individuals, n_timepoints,
perturbation_timepoint, verbose)
def visualization(output_dir: str, pcoa: str, metadata: str,
individual_col: str, timepoint_col: str, treatment_col: str):
"""
Connect to karenina.visualization and save output
:param output_dir: output directory
:param pcoa: pcoa qza file
:param metadata: metadata file location
:param individual_col: individual column identifiers
:param timepoint_col: timepoint column identifier
:param treatment_col: treatment column identifier
"""
# Parse in pcoa and metadata as dataframes and inject to k_visualization
ind = []
site = _parse_pcoa(pcoa)
df = _parse_metadata(metadata, individual_col, timepoint_col,
treatment_col, site)
i = 0
colors = ['fuchsia', 'cyan', 'darkorange', 'blue', 'yellow']
tx = treatment_col
treatments = df[tx].unique()
while len(colors) < len(treatments):
colors.append('lightgray')
for row in df.iterrows():
curr_subject_id = "%s_%i" % (df[individual_col], i)
j = 0
while row[1][3] != treatments[j]:
j += 1
color = colors[j]
params = {
'lambda': 0.2,
'delta': 0.25,
'interindividual_variation': 0.01
}
params['color'] = color
curr_subject = Individual(subject_id = curr_subject_id,
params = params, \
metadata = {treatment_col: df[treatment_col]}, \
interindividual_variation = .01)
ind.append(curr_subject)
i += 1
k_visualization.save_simulation_figure(individuals=ind,
output_folder=output_dir,
n_timepoints=50,
perturbation_timepoint=25,
n_individuals=50)
k_visualization.save_simulation_movie(individuals=ind,
output_folder=output_dir,
n_timepoints=50,
n_individuals=50)